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Classication of Wisconsin Breast Cancer Diagnosticand Prognostic Dataset using Polynomial Neural

Network

A Dissertation WorkSubmitted in Partial fulllment for t he aw ard of

Post Graduate Degree of Master of TechnologyIn Computer Science & Engineering

Submitted to

Rajiv Gandhi Proudyogiki Vishwavidhyalaya,Bhopal (M.P.)

Submitted By:Shweta Saxena

0126CS10MT17

Under the Guidance of Dr. Kavita Burse

Director, OCT, Bhopal.

Department of Computer Science & Engineering



ORIENTAL COLLGEGE OF TECHNOLOGY,BHOPAL

(Formerly known as Thakral College of Technology, Bhopal)

Approved by AICTE New Delhi & Govt. of M.P. Affi liated to Rajiv Gandhi Proudyogiki Vishwavidhyalaya, Bhopal (M.P.)

Session 2012-13

ORIENTAL COLLGEGE OF TECHNOLOGY, BHOPAL(Formerly known as Thakral College of Technology, Bhopal)

Approved by AICTE New Delhi & Govt. of M.P. and Affi liated to RajivGandhi Proudyogiki Vishwavidhyalaya Bhopal (M.P.)

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

CERTIFICATE

THIS IS TO CERTIFY THAT THE DISSERTATION ENTITLED

“Classication of Wisconsin Breast CancerDiagnostic and Prognostic Dataset using PolynomialNeural Network ” BEING SUBMITTED BY Shweta SaxenaIN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR

THE AWARD OF M.TECH DEGREE IN COMPUTERSCIENCE & ENGINEERING TO ORIENTAL COLLEGE OF

TECHNOLOGY, BHOPAL (M.P) IS A RECORD OF BONAFIDE WORK DONE BY HIM UNDER MY GUIDANCE.

II



Dr. Kavita Burse Prof. RoopaliSoni

Dire !or "ead o#Depar!$en!, %&'

% , Bhopal % , Bhopal (Guide)

ORIENTAL COLLGEGE OF TECHNOLOGY, BHOPAL(Formerly known as Thakral College of Technology, Bhopal)

Approved by AICTE New Delhi & Govt. of M.P. and Affi liated to RajivGandhi Proudyogiki Vishwavidhyalaya Bhopal (M.P.)

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

APPROVAL CERTIFICATE

This dissertation work entitled “Classication of

Wisconsin Breast Cancer Diagnostic and Prognostic

Dataset using Polynomial Neural Network ” submitted

by Shweta Saxena is approved for the award of degree of

Master of Technology in Computer Science & Engineering.

III



INTERNAL EXAMINER EXTERNAL EXAMINER

Date: Date:

IV



CANDIDATE DECLARATION

I hereby declare t hat t he dissertation work presented in the report

entitled as “Classication of Wisconsin Breast Cancer Diagnosticand Prognostic Dataset using Polynomial Neural Network”submitted in the partial fulllment of the requirements for t he aw ardof the degree of Master of Technology in Computer Science &Engineering of Oriental College of Technology is a n authentic recordof my own work.

I have n ot submitted the p art and partial of this report for t he award

of any other degree or diploma.

Date: ShwetaSaxena

(0126CS10MT17)

This is to certify that the above statement made by the correct to the best the best of my knowledge.

Dr. Kavita BurseDirector

OCT, Bhopal(Guide)

V



ACKNOWLEDGEMENT

I would like !o e*press $y deep sense o# respe ! and gra!i!ude !owards $y advisor

and guide Dr. Kavita Burse Dire!tor Oriental Colle"e of Te!#nolo"$ who has

given $e an oppor!uni!y !o work under her. &he has +een a ons!an! sour e o#

inspira!ion !hroughou! $y work. &he displayed uni ue !oleran e and unders!anding a!

every s!ep o# progress o# !his work and en ouraged $e in essan!ly. "er invalua+le

knowledge and innova!ive ideas helped $e !o !ake !he work !o !he #inal s!age. I

onsider i! $y good #or!une work under su h a wonder#ul person.

I e*press $y respe ! !o Prof. Roopali Soni %ea& Co'puter S!ien!e

En"ineerin" Depart'ent Oriental Colle"e of Te!#nolo"$ #or her ons!an!

en ourage$en! and invalua+le advi e in every aspe ! o# $y a ade$i li#e. I a$ also

!hank#ul !o all #a ul!y $e$+ers o# %o$pu!er & ien e and 'ngineering Depar!$en! #or

!heir suppor! and guidan e.

I a$ espe ially !hank#ul !o $y #a!her Mr. Da'o&ar Sa(ena , $y $o!her Mrs.

Nir'ala Sa(ena , and $y loving sis!ers S#i)#a and S#ra&&#a #or !heir love,

sa ri#i e and suppor! on every pa!h o# $y li#e. I e*!end a spe ial word o# !hanks !o $y

hus+and Mr. As#is# Sa(ena #or his $oral suppor! and help in a hieving $y ai$.

-as! +u! no! !he leas! I a$ e*!re$ely !hank#ul !o all who have dire !ly or indire !ly

helped $e #or !he o$ple!ion o# $y work.

&hwe!a &a*ena ( /01%&/ M /2)

VI



ORGAN*+AT*ON O, D*SSERTAT*ON

he repor! 3 Classifi!ation of Wis!onsin Dia"nosti! an& Pro"nosti! Dataset usin"

Pol$no'ial Neural Net-or) has +een divided in!o 2 hap!ers as #ollows4

C#apter / *ntro&u!tion

%hap!er / #irs! des ri+es !he $o!iva!ion o# !his resear h work. I! !hen des ri+es +reas!

an er disease, i!s sy$p!o$s and !ypes in de!ail. he hap!er also des ri+es diagnosis

and prognosis pro ess o# !he disease.

C#apter / Literature Revie-

Di##eren! 5eural ne!work !e hni ues #or diagnosis and prognosis o# +reas! an er

diagnosis and prognosis are des ri+ed in !his hap!er along wi!h !he rela!ed work

on erned wi!h !hese !e hni ues. he hap!er also o$pares !he a ura ies o#

di##eren! !e hni ues a! !he end.

C#apter 0 Artifi!ial Neural Net-or) an& Prin!ipal Co'ponent

Anal$sis

In !his hap!er 6r!i#i ial 5eural ne!work is des ri+ed in de!ail along wi!h i!s

advan!ages and $edi al appli a!ions. he hap!er des ri+es in de!ail !he higher order

or polyno$ial neural ne!work along wi!h +a k propaga!ion algori!h$ whi h are used

in !his resear h #or lassi#i a!ion. he hap!er ne*! provides !he de!ailed in#or$a!ion

a+ou! da!a prepro essing !e hni ue na$ed Prin ipal %o$ponen! 6nalysis and i!s

advan!ages.

C#apter 1 MATLAB

he !e hnology used #or i$ple$en!a!ion o# proposed work is M6 -6B. he hap!er

gives a +rie# in!rodu !ion o# M6 -6B along wi!h i!s advan!ages and de!ailed

des rip!ion o# 5eural 5e!work ool+o* availa+le in M6 -6B #or design o# neural

ne!work. he hap!er also e*plains !he neural ne!work design pro ess using neural

ne!work !ool+o*.

VII



C#apter 2

%hap!er 7 presen!s !he des rip!ion o# da!ase! used #or i$ple$en!a!ion o# !his resear h

and !he resul!s o# i$ple$en!a!ion.

C#apter 3

%hap!er 1 on ludes !he disser!a!ion and provides possi+le dire !ions #or relevan!

#u!ure work.

VIII



ABSTRACT

Breas! an er is !he $os! o$$on #or$ o# an er and $ajor ause o# dea!h inwo$en. 5or$ally, !he ells o# !he +reas! divide in a regula!ed $anner. I# ells keepon dividing when new ells are no! needed, a $ass o# !issue #or$s. his $ass is

alled a !u$or. his !u$or an +e an erous or non8 an erous. he goal o# diagnosisis !o dis!inguish +e!ween an erous and non8 an erous ells. n e a pa!ien! isdiagnosed wi!h +reas! an er, !he prognosis gives !he an!i ipa!ed long8!er$ +ehavior o# !he ail$en!. Breas! an er de!e !ion, lassi#i a!ion, s oring and grading o# his!opa!hologi al i$ages is !he s!andard lini al pra !i e #or !he diagnosis and

prognosis o# +reas! an er. In a large hospi!al, a pa!hologis! !ypi ally handles anu$+er o# an er de!e !ion ases per day. I! is, !here#ore, a very di##i ul! and !i$e8

onsu$ing !ask. wing !o !heir wide range o# appli a+ili!y and !heir a+ili!y !o learno$ple* and non linear rela!ionships in luding noisy or less pre ise in#or$a!ion

6r!i#i ial 5eural 5e!works (655s) are very well sui!ed !o solve pro+le$s in

+io$edi al engineering. 655s an +e applied !o $edi ine in #our +asi #ields4$odeling, +ioele !ri signal pro essing, diagnosing and prognos!i s. here areseveral sys!e$s availa+le #or !he diagnosis and sele !ion o# !herapeu!i s!ra!egies in

+reas! an er.

In !his resear h we propose neural ne!work +ased lini al suppor! sys!e$ !o provide$edi al da!a analysis #or diagnosis and prognosis o# +reas! an er. he sys!e$

lassi#ies !he +reas! an er diagnos!i da!a whi h are provided as inpu! !o neuralne!work in!o !wo se!s8 +enign (non8 an erous) and $alignan! ( an erous) !o ge! !hediagnos!i resul!s. 9or ge!!ing prognosis resul!s !he sys!e$ lassi#y !he prognos!ida!a whi h are given as inpu! !o neural ne!work in!o !wo lasses8 re urren! and non8

re urren!. Resul!s +elong !o re urren! se! shows !ha! an er is reo urred a#!er so$e!i$e. Polyno$ial neural ne!work (P55) s!ru !ure is used along wi!h +a k propaga!ion algori!h$ #or lassi#i a!ion o# +reas! an er da!a. :is onsin Breas!%an er (:B%) da!ase!s #ro$ !he ;%I Ma hine -earning reposi!ory is used as inpu!da!ase!s !o P55. Da!a pre8pro essing !e hni ue na$ed Prin ipal %o$ponen!6nalysis (P%6) is used as a #ea!ures redu !ion !rans#or$a!ion $e!hod !o i$prove !hea ura y o# P55. In our resul!s !he Mean & uare error (M&') is su+s!an!iallyredu ed #or P%6 prepro essed da!a as o$pared !o nor$ali<ed da!a. "en e we ge!$ore a ura!e diagnosis and prognosis resul!s.

Ke$-or&s 8 4reast !an!er pol$no'ial neural net-or) prin!ipal !o'ponent

anal$sis -is!onsin 4reast !an!er &ataset.

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